In [125]:
plt.plot(data[' PORT4_CHN1'], label = 'PORT4_CHN1',c='royalblue')
plt.legend()
Out[125]:
<matplotlib.legend.Legend at 0x160ae12d0>
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data = pd.read_csv("/Users/kimdaehun/Desktop/dataset/ds_hw1_dataset.csv")
data
| nSeq | DI | PORT4_CHN1 | PORT6_CHN1 | PORT7_CHN1 | |
|---|---|---|---|---|---|
| 0 | 0 | 0 | NaN | -0.4806 | 0.3770 |
| 1 | 1 | 0 | -0.2104 | -0.4819 | 0.3759 |
| 2 | 2 | 0 | -0.2103 | -0.4822 | NaN |
| 3 | 3 | 0 | -0.2119 | -0.4819 | 0.3806 |
| 4 | 4 | 0 | -0.2148 | -0.4812 | NaN |
| ... | ... | ... | ... | ... | ... |
| 30445 | 30445 | 0 | NaN | -0.6162 | 0.1777 |
| 30446 | 30446 | 0 | -0.0034 | -0.6157 | 0.1780 |
| 30447 | 30447 | 0 | -0.0037 | -0.6145 | 0.1772 |
| 30448 | 30448 | 0 | 0.0000 | -0.6157 | 0.1820 |
| 30449 | 30449 | 0 | 0.0005 | -0.6144 | 0.1855 |
30450 rows × 5 columns
plt.plot(data[' PORT4_CHN1'], label = 'PORT4_CHN1',c='royalblue')
plt.legend()
<matplotlib.legend.Legend at 0x160ae12d0>
데이터 에서 PORT4_CHN1 에 해당하는 열의 값들을 그래프로 나타낸 것으로, x축은 index, y축은 해당 데이터 값이다. 그래프를 보았을때 군데군데 비어 있는 부분이 보이는 것을 볼 수 있고, 이는 데이터 값중에서 NaN으로 인해 발생한 것이다.
plt.plot(data[' PORT6_CHN1'], label = 'PORT6_CHN1',c='orangered')
plt.legend()
<matplotlib.legend.Legend at 0x1609934c0>
데이터 에서 PORT6_CHN1 에 해당하는 열의 값들을 그래프로 나타낸 것으로, x축은 index, y축은 해당 데이터 값이다. 그래프를 보았을때 군데군데 비어 있는 부분이 보이는 것을 볼 수 있고, 이는 데이터 값중에서 NaN으로 인해 발생한 것이다.
plt.plot(data[' PORT7_CHN1'], label = 'PORT7_CHN1',c='seagreen')
plt.legend()
<matplotlib.legend.Legend at 0x13d313d00>
데이터 에서 PORT7_CHN1 에 해당하는 열의 값들을 그래프로 나타낸 것으로, x축은 index, y축은 해당 데이터 값이다. 그래프를 보았을때 군데군데 비어 있는 부분이 보이는 것을 볼 수 있고, 이는 데이터 값중에서 NaN으로 인해 발생한 것이다.
# fill the blank code
data.isnull().sum() #결측치 확인
nSeq 0 DI 0 PORT4_CHN1 10141 PORT6_CHN1 2834 PORT7_CHN1 6769 dtype: int64
isnull 을 통해 결측치를 보고, sum()을 통해 그 양을 더한다음 보였다.
A) Delete data with missing values by using pandas.dropna().
# fill the blank code
Adata = data.copy() #데이터 복사본
Adata= Adata.dropna() # dropna를 통한 결측값 제거
print(Adata)
nSeq DI PORT4_CHN1 PORT6_CHN1 PORT7_CHN1 1 1 0 -0.2104 -0.4819 0.3759 3 3 0 -0.2119 -0.4819 0.3806 8 8 0 -0.2209 -0.4796 0.3862 9 9 0 -0.2227 -0.4794 0.3859 10 10 0 -0.2253 -0.4786 0.3881 ... ... ... ... ... ... 30444 30444 0 -0.0078 -0.6147 0.1741 30446 30446 0 -0.0034 -0.6157 0.1780 30447 30447 0 -0.0037 -0.6145 0.1772 30448 30448 0 0.0000 -0.6157 0.1820 30449 30449 0 0.0005 -0.6144 0.1855 [14249 rows x 5 columns]
원본 데이터 파일 유지를 위해 copy 를 하고 카피된 데이터를 dropna()를 통해 결측치가 있는 index들을 다 빼버렸다.
B) Replace the missing value with the average value within the window size (window=50) before and after the interval with missing values for each parameter.
def get_average(data, column, index, window): #들어오는 index 값 기준 앞 뒤 50개의 데이터 값의 평균을 구하는 함수
wlist = [] #윈도우에 해당하는 데이터를 넣을 리스트 정의 및 초기화
for i in range(index-window,index+1+window,1): #index기준 앞 뒤 window 크기 만큼, 증분 1
if(0<i<len(data) )== True: #out of range 방지, i 가 데이터 인덱스 내에 존재할때만 계산
if (data[column][i] < 0 or data[column][i] >= 0) == False: #해당 데이터 값이 결측 값인지 여부 판단
continue
else: #결측치가 아니라면 리스트에 추가한다
wlist.append(data[column][i])
avg_array = np.array(wlist) #array화 시켜준다.
avg = np.mean(avg_array) #np.mean()을 이용해 평균을 구한다
return avg #평균값 반환
Bdata = data.copy() #Bdata를 data를 copy해 초기화한다
window = 50 # window size를 과제에 명시한 대로 50이라 설정한다.
for column in [' PORT4_CHN1', ' PORT6_CHN1', ' PORT7_CHN1']: #3개의 column을 돌며
for index in range(len(Bdata)): #index를 데이터 길이만큼 증가시키면서 값들을 확인
if (Bdata[column][index] < 0 or Bdata[column][index] >= 0) == False: #값이 결측치 임을 판단하면
Bdata[column][index] = get_average(data,column, index, window) #결측치를 문제 요구 사항대로 평균 값으로 변환한다.
/var/folders/5c/kcr959w56zz_1p5rkcrjp7dc0000gn/T/ipykernel_18222/1856698464.py:7: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy Bdata[column][index] = get_average(data,column, index, window) #결측치를 문제 요구 사항대로 평균 값으로 변환한다.
B단계는 결측치를 평균 값으로 대체하는 방법이다. get_average()함수를 만들어, 특정 인덱스 값 기준 윈도우 크기 만큼 전 후를 반복문을 통해 탐색한다. 이후 값들이 데이터 크기 내에 존재한다면, 결측값이라면 일단 넘어가고 결측치라고 판단되면 wlist라는 비어있는 리스트안에 집어 넣는다. 그 후 wlist를 array화 시킨다음 np.mean을 이용해 평균을 구하고 이를 함수에서 리턴시긴다. 그 후 마찬가지로 데이터를 Bdata로 카피하고, 3개의 데이터 프레임 열을 순차적으로 돌면서 각 인덱스를 확인하고, 결측치임을 판단하면 이번에는 get_average()를 통해 바뀐 값으로 결측치를 대체해서 결측치를 없애는 것이다.
print("before")
print(data) #이전 데이터
print("after")
print(Bdata) #평균값으로 클렌징한 데이터
before
nSeq DI PORT4_CHN1 PORT6_CHN1 PORT7_CHN1
0 0 0 NaN -0.4806 0.3770
1 1 0 -0.2104 -0.4819 0.3759
2 2 0 -0.2103 -0.4822 NaN
3 3 0 -0.2119 -0.4819 0.3806
4 4 0 -0.2148 -0.4812 NaN
... ... ... ... ... ...
30445 30445 0 NaN -0.6162 0.1777
30446 30446 0 -0.0034 -0.6157 0.1780
30447 30447 0 -0.0037 -0.6145 0.1772
30448 30448 0 0.0000 -0.6157 0.1820
30449 30449 0 0.0005 -0.6144 0.1855
[30450 rows x 5 columns]
after
nSeq DI PORT4_CHN1 PORT6_CHN1 PORT7_CHN1
0 0 0 -0.227787 -0.4806 0.377000
1 1 0 -0.210400 -0.4819 0.375900
2 2 0 -0.210300 -0.4822 0.405217
3 3 0 -0.211900 -0.4819 0.380600
4 4 0 -0.214800 -0.4812 0.405217
... ... ... ... ... ...
30445 30445 0 -0.073443 -0.6162 0.177700
30446 30446 0 -0.003400 -0.6157 0.178000
30447 30447 0 -0.003700 -0.6145 0.177200
30448 30448 0 0.000000 -0.6157 0.182000
30449 30449 0 0.000500 -0.6144 0.185500
[30450 rows x 5 columns]
nan값이 없어지고, 요구사항에 부합하는 평균값으로 대체된 것을 확인할 수 있다.
C) Replace the missing value with the median value within the window size (window=50) before and after the interval with missing values for each parameter.
def get_median(data, column, index, window): #들어오는 index 값 기준 앞 뒤 50개의 데이터 값의 median 값을 구하는 함수
wlist = [] #윈도우에 해당하는 데이터를 넣을 리스트 정의 및 초기화
for i in range(index-window,index+1+window,1): #index 기준 앞 뒤 window 크기 만큼 증분,1
if(0<i<len(data) )== True: #해당 데이터 값이 결측 값인지 여부 판다
if (data[column][i] < 0 or data[column][i] >= 0) == False:
continue
else: #결측치가 아니라면 리스트에 추가한다
wlist.append(data[column][i])
median_array = np.array(wlist) #array화 시켜준다.
mid = np.median(median_array) #np.mean()을 이ㅛㅇ해 median 값을 구한다
return mid #median 값 반환
Cdata = data.copy() #Cdata를 data를 카피해 초기화한다.
window = 50 # window size는 과제에 명시된대로 50이라 설정한다.
for column in [' PORT4_CHN1', ' PORT6_CHN1', ' PORT7_CHN1']: #column을 돌면서
for index in range(len(Cdata)): #index를 데이터 길이만큼 증가시키면서 데이터값을 확인
if (Cdata[column][index] < 0 or Cdata[column][index] >= 0) == False: #값이 결측치 임을 판단하면
Cdata[column][index] = get_median(data,column, index, window) #결측치를 문제 요구 사항 대로 median값으로 해당 값을 변경한다
/var/folders/5c/kcr959w56zz_1p5rkcrjp7dc0000gn/T/ipykernel_18222/98410052.py:7: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy Cdata[column][index] = get_median(data,column, index, window) #결측치를 문제 요구 사항 대로 median값으로 해당 값을 변경한다
C단계는 결측치를 median 값으로 대체하는 방법이다. get_median()함수를 만들어, 특정 인덱스 값 기준 윈도우 크기 만큼 전 후를 반복문을 통해 탐색한다. 이후 값들이 데이터 크기 내에 존재한다면, 결측값이라면 일단 넘어가고 결측치라고 판단되면 wlist라는 비어있는 리스트안에 집어 넣는다. 그 후 wlist를 array화 시킨다음 np.median을 이용해 평균을 구하고 이를 함수에서 리턴시긴다. 그 후 마찬가지로 데이터를 Cdata로 카피하고, 3개의 데이터 프레임 열을 순차적으로 돌면서 각 인덱스를 확인하고, 결측치임을 판단하면 이번에는 get_median()를 통해 바뀐 값으로 결측치를 대체해서 결측치를 없애는 것이다.
print("before")
print(data) #이전 원본 데이터
print("after")
print(Cdata) # median 값으로 클렌징한 데이터
before
nSeq DI PORT4_CHN1 PORT6_CHN1 PORT7_CHN1
0 0 0 NaN -0.4806 0.3770
1 1 0 -0.2104 -0.4819 0.3759
2 2 0 -0.2103 -0.4822 NaN
3 3 0 -0.2119 -0.4819 0.3806
4 4 0 -0.2148 -0.4812 NaN
... ... ... ... ... ...
30445 30445 0 NaN -0.6162 0.1777
30446 30446 0 -0.0034 -0.6157 0.1780
30447 30447 0 -0.0037 -0.6145 0.1772
30448 30448 0 0.0000 -0.6157 0.1820
30449 30449 0 0.0005 -0.6144 0.1855
[30450 rows x 5 columns]
after
nSeq DI PORT4_CHN1 PORT6_CHN1 PORT7_CHN1
0 0 0 -0.22925 -0.4806 0.3770
1 1 0 -0.21040 -0.4819 0.3759
2 2 0 -0.21030 -0.4822 0.4084
3 3 0 -0.21190 -0.4819 0.3806
4 4 0 -0.21480 -0.4812 0.4084
... ... ... ... ... ...
30445 30445 0 -0.07690 -0.6162 0.1777
30446 30446 0 -0.00340 -0.6157 0.1780
30447 30447 0 -0.00370 -0.6145 0.1772
30448 30448 0 0.00000 -0.6157 0.1820
30449 30449 0 0.00050 -0.6144 0.1855
[30450 rows x 5 columns]
마찬가지로 nan 값이 사라지고 요구 조건에 부합하는 median값으로 결측치가 대체된 것을 확인할 수 있다.
D) Replace the missing value with the max value within the window size (window=50) before and after the interval with missing values for each parameter.
def get_max(data, column, index, window): #들어오는 index 값 기준 앞 뒤 50개의 데이터 값의 최대 값을 구하는 함수
wlist = [] #윈도우에 해당하는 데이터를 넣을 리스트 정의 및 초기화
for i in range(index-window,index+1+window,1): #index 기준 앞 뒤 window 크기만큼 증분 1씩
if(0<i<len(data) )== True: #out of rnage 방지, i 가 데이터 인덱스 내에 존재할때만 계산
if (data[column][i] < 0 or data[column][i] >= 0) == False: #해당 데이터 값이 결측 값인지 여부 판단
continue
else: #결측치가 아니라면 리스트에 추가한다.
wlist.append(data[column][i])
max_array = np.array(wlist) #array화 시켜준다.
max = np.max(max_array) #np.max()를 사용해 최대 값을 구한다
return max #최대값 반환
Ddata = data.copy() #Ddata를 data를 copy 해 초기화한다.
window = 50 #window size를 과제에 명시한 대로 50이라 설정한다.
for column in [' PORT4_CHN1', ' PORT6_CHN1', ' PORT7_CHN1']: #3개의 column을 돌며
for index in range(len(Ddata)): #index를 데이터 길이만큼 증가시키면서 값들을 확인
if (Ddata[column][index] < 0 or Ddata[column][index] >= 0) == False: # 값이 결측치 임을 판단하면
Ddata[column][index] = get_max(data,column, index, window) #결측치를 문제 요구사항대로 최대 값으로 변경
/var/folders/5c/kcr959w56zz_1p5rkcrjp7dc0000gn/T/ipykernel_18222/3305863182.py:7: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy Ddata[column][index] = get_max(data,column, index, window) #결측치를 문제 요구사항대로 최대 값으로 변경
D단계는 결측치를 max 값으로 대체하는 방법이다. get_max()함수를 만들어, 특정 인덱스 값 기준 윈도우 크기 만큼 전 후를 반복문을 통해 탐색한다. 이후 값들이 데이터 크기 내에 존재한다면, 결측값이라면 일단 넘어가고 결측치라고 판단되면 wlist라는 비어있는 리스트안에 집어 넣는다. 그 후 wlist를 array화 시킨다음 np.max을 이용해 평균을 구하고 이를 함수에서 리턴시긴다. 그 후 마찬가지로 데이터를 Ddata로 카피하고, 3개의 데이터 프레임 열을 순차적으로 돌면서 각 인덱스를 확인하고, 결측치임을 판단하면 이번에는 get_max()를 통해 바뀐 값으로 결측치를 대체해서 결측치를 없애는 것이다.
print("before")
print(data) #이전 데이터
print("after")
print(Ddata) #최대값으로 클렌징한 데이터
before
nSeq DI PORT4_CHN1 PORT6_CHN1 PORT7_CHN1
0 0 0 NaN -0.4806 0.3770
1 1 0 -0.2104 -0.4819 0.3759
2 2 0 -0.2103 -0.4822 NaN
3 3 0 -0.2119 -0.4819 0.3806
4 4 0 -0.2148 -0.4812 NaN
... ... ... ... ... ...
30445 30445 0 NaN -0.6162 0.1777
30446 30446 0 -0.0034 -0.6157 0.1780
30447 30447 0 -0.0037 -0.6145 0.1772
30448 30448 0 0.0000 -0.6157 0.1820
30449 30449 0 0.0005 -0.6144 0.1855
[30450 rows x 5 columns]
after
nSeq DI PORT4_CHN1 PORT6_CHN1 PORT7_CHN1
0 0 0 -0.2103 -0.4806 0.3770
1 1 0 -0.2104 -0.4819 0.3759
2 2 0 -0.2103 -0.4822 0.4258
3 3 0 -0.2119 -0.4819 0.3806
4 4 0 -0.2148 -0.4812 0.4258
... ... ... ... ... ...
30445 30445 0 0.0005 -0.6162 0.1777
30446 30446 0 -0.0034 -0.6157 0.1780
30447 30447 0 -0.0037 -0.6145 0.1772
30448 30448 0 0.0000 -0.6157 0.1820
30449 30449 0 0.0005 -0.6144 0.1855
[30450 rows x 5 columns]
결측 치가 사라지고 특정 인덱스 범위 내의 max값이 결측치를 대체하는 것을 확인할 수 있다.
E) Replace the missing value with the min value within the window size (window=50) before and after the interval with missing values for each parameter.
def get_min(data, column, index, window): #들어오는 index 값 기준 앞 뒤 50개의 데이터 값의 최솟값을 구하는 함수
wlist = [] #윈도우에 해당하는 데이터를 넣을 리스트 정의 및 초기화
for i in range(index-window,index+1+window,1): #index 기준 앞 뒤 window 크기 만큼 증분 1
if(0<i<len(data) )== True: #out of range 방지, i 가 데이터 인덱스 내에 존재할 때만 계산
if (data[column][i] < 0 or data[column][i] >= 0) == False: # 해당 데이터 값이 결측 값인지 여부 판단
continue
else: # 결측 치가 아니라면 리스트에 추가한다.
wlist.append(data[column][i])
min_array = np.array(wlist) #array화 시켜준다.
min = np.min(min_array) #np.min() 을 이용해 최솟 값을 구한다
return min # 최솟값 반환
Edata = data.copy() #Edata를 data 를 copy 해 초기화한다.
window = 50 # window size 를 과제에 명시한 대로 50이라 설정한다.
for column in [' PORT4_CHN1', ' PORT6_CHN1', ' PORT7_CHN1']: #3개의 column 을 돌며
for index in range(len(Edata)): #index를 데이터 길이만큼 증가시키며 값들을 확인
if (Edata[column][index] < 0 or Edata[column][index] >= 0) == False: #값이 결측치 임을 판단하면
Edata[column][index] = get_min(data,column, index, window) #결측치를 문제 요구사항대로 최솟 값으로 변경
/var/folders/5c/kcr959w56zz_1p5rkcrjp7dc0000gn/T/ipykernel_18222/1074089312.py:7: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy Edata[column][index] = get_min(data,column, index, window) #결측치를 문제 요구사항대로 최솟 값으로 변경
E단계는 결측치를 min 값으로 대체하는 방법이다. get_min()함수를 만들어, 특정 인덱스 값 기준 윈도우 크기 만큼 전 후를 반복문을 통해 탐색한다. 이후 값들이 데이터 크기 내에 존재한다면, 결측값이라면 일단 넘어가고 결측치라고 판단되면 wlist라는 비어있는 리스트안에 집어 넣는다. 그 후 wlist를 array화 시킨다음 np.min을 이용해 평균을 구하고 이를 함수에서 리턴시긴다. 그 후 마찬가지로 데이터를 Edata로 카피하고, 3개의 데이터 프레임 열을 순차적으로 돌면서 각 인덱스를 확인하고, 결측치임을 판단하면 이번에는 get_min()를 통해 바뀐 값으로 결측치를 대체해서 결측치를 없애는 것이다.
print("before")
print(data) #이전 원본 데이터
print("after")
print(Edata) #최솟값으ㅗ 클렌징한 데이터
before
nSeq DI PORT4_CHN1 PORT6_CHN1 PORT7_CHN1
0 0 0 NaN -0.4806 0.3770
1 1 0 -0.2104 -0.4819 0.3759
2 2 0 -0.2103 -0.4822 NaN
3 3 0 -0.2119 -0.4819 0.3806
4 4 0 -0.2148 -0.4812 NaN
... ... ... ... ... ...
30445 30445 0 NaN -0.6162 0.1777
30446 30446 0 -0.0034 -0.6157 0.1780
30447 30447 0 -0.0037 -0.6145 0.1772
30448 30448 0 0.0000 -0.6157 0.1820
30449 30449 0 0.0005 -0.6144 0.1855
[30450 rows x 5 columns]
after
nSeq DI PORT4_CHN1 PORT6_CHN1 PORT7_CHN1
0 0 0 -0.2400 -0.4806 0.3770
1 1 0 -0.2104 -0.4819 0.3759
2 2 0 -0.2103 -0.4822 0.3759
3 3 0 -0.2119 -0.4819 0.3806
4 4 0 -0.2148 -0.4812 0.3759
... ... ... ... ... ...
30445 30445 0 -0.1462 -0.6162 0.1777
30446 30446 0 -0.0034 -0.6157 0.1780
30447 30447 0 -0.0037 -0.6145 0.1772
30448 30448 0 0.0000 -0.6157 0.1820
30449 30449 0 0.0005 -0.6144 0.1855
[30450 rows x 5 columns]
결측 치가 사라지고 특정 인덱스 범위 내의 min값이 결측치를 대체하는 것을 확인할 수 있다.
A) Plot
data
| nSeq | DI | PORT4_CHN1 | PORT6_CHN1 | PORT7_CHN1 | |
|---|---|---|---|---|---|
| 0 | 0 | 0 | NaN | -0.4806 | 0.3770 |
| 1 | 1 | 0 | -0.2104 | -0.4819 | 0.3759 |
| 2 | 2 | 0 | -0.2103 | -0.4822 | NaN |
| 3 | 3 | 0 | -0.2119 | -0.4819 | 0.3806 |
| 4 | 4 | 0 | -0.2148 | -0.4812 | NaN |
| ... | ... | ... | ... | ... | ... |
| 30445 | 30445 | 0 | NaN | -0.6162 | 0.1777 |
| 30446 | 30446 | 0 | -0.0034 | -0.6157 | 0.1780 |
| 30447 | 30447 | 0 | -0.0037 | -0.6145 | 0.1772 |
| 30448 | 30448 | 0 | 0.0000 | -0.6157 | 0.1820 |
| 30449 | 30449 | 0 | 0.0005 | -0.6144 | 0.1855 |
30450 rows × 5 columns
c=0 #c는 0으로 초기화
color=['royalblue','orangered','seagreen'] #컬러 리스트 세개 선언
title=['before','after'] #제목 리스트 두개 선언
for column in [' PORT4_CHN1', ' PORT6_CHN1', ' PORT7_CHN1']: #column을 돌면서
fig, ax= plt.subplots(1,2,figsize=(20,5)) #1X2 그래프 작성, 사이즈는 20,10
i=0 #초기화
t=0
for datalist in[data,Adata]: #원본 데이터와 Adata만 활용
ax[i].plot(datalist[column],label=column,color=color[c]) #반복문을 통해 data, Adata 각각 플랏
ax[i].legend(loc='lower right') # 라벨링 오른쪽 밑
ax[i].set_title(title[t]) #타이틀 리스트를 통해 비포 애프터 제목 라벨링
if(i==0): #1X2 서브플랏이므로 ax [0], ax[1] 로 나눌 수 있다. 따라서 i=0 i=1 일때 t 와 i 로 0,1을 조절하고 이를통해 리스트에서 원하는ㄴ 요소를 추출한다.
i+=1
t+=1
else:
i-=1
t-=1
c += 1
fig.tight_layout() #그래프 간격 유지
플랏 A 에 관한 결과 분석을 먼저 해보겠다. 코드에 관한 설명은 주석을 참고하면 될 것 같다. 이 부분에서는 결측치를 dropna를 통해 결측치였던 부분을 다 제거하고 플랏을 찍은 것으로, 육안으로 봐도 x축의 값들이 좀 비는 것을 확인할 수있다.
B) Plot
c=0 # A단계 플롯 주석과 동일
color=['royalblue','orangered','seagreen'] #컬러 리스트 세개 선언
title=['before','after']
for column in [' PORT4_CHN1', ' PORT6_CHN1', ' PORT7_CHN1']:
fig, ax= plt.subplots(1,2,figsize=(20,5))
i=0
t=0
for datalist in[data,Bdata]:
ax[i].plot(datalist[column],label=column,color=color[c])
ax[i].legend(loc='lower right')
ax[i].set_title(title[t])
if(i==0):
i+=1
t+=1
else:
i-=1
t-=1
c += 1
fig.tight_layout() #그래프 간격 유지
B단게를 plot한 것이다. 전과 비교하면 결측치로 인해 비었던 흰부분이 채워졌으며, A와 비교해도 더 빼곡하다는 것을 확인할 수 있다.
C) Plot
c=0# A단계 플롯 주석과 동일
color=['royalblue','orangered','seagreen'] #컬러 리스트 세개 선언
title=['before','after']
for column in [' PORT4_CHN1', ' PORT6_CHN1', ' PORT7_CHN1']:
fig, ax= plt.subplots(1,2,figsize=(20,5))
i=0
t=0
for datalist in[data,Cdata]:
ax[i].plot(datalist[column],label=column,color=color[c])
ax[i].legend(loc='lower right')
ax[i].set_title(title[t])
if(i==0):
i+=1
t+=1
else:
i-=1
t-=1
c += 1
fig.tight_layout() #그래프 간격 유지
C 단계, 즉 결측치를 median 값으로 대체한 것을 plot 한 것이다. 마찬가지로 결측치로 비었던 부분들이 채워진것을 볼 수있다. 다른 단계들과 정확한 비교를 위해 차후 밑에서 plot했던 것들을 다 모은다음 비교하도록 하겠다.
D) Plot
c=0# A단계 플롯 주석과 동일
color=['royalblue','orangered','seagreen'] #컬러 리스트 세개 선언
title=['before','after']
for column in [' PORT4_CHN1', ' PORT6_CHN1', ' PORT7_CHN1']:
fig, ax= plt.subplots(1,2,figsize=(20,5))
i=0
t=0
for datalist in[data,Ddata]:
ax[i].plot(datalist[column],label=column,color=color[c])
ax[i].legend(loc='lower right')
ax[i].set_title(title[t])
if(i==0):
i+=1
t+=1
else:
i-=1
t-=1
c += 1
fig.tight_layout() #그래프 간격 유지
D 단계, 즉 결측치를 max 값으로 대체한 것을 plot 한 것이다. 마찬가지로 결측치로 비었던 부분들이 채워진것을 볼 수있다. 다른 단계들과 정확한 비교를 위해 차후 밑에서 plot했던 것들을 다 모은다음 비교하도록 하겠다.
E) Plot
c=0# A단계 플롯 주석과 동일
color=['royalblue','orangered','seagreen'] #컬러 리스트 세개 선언
title=['before','after']
for column in [' PORT4_CHN1', ' PORT6_CHN1', ' PORT7_CHN1']:
fig, ax= plt.subplots(1,2,figsize=(20,5))
i=0
t=0
for datalist in[data,Edata]:
ax[i].plot(datalist[column],label=column,color=color[c])
ax[i].legend(loc='lower right')
ax[i].set_title(title[t])
if(i==0):
i+=1
t+=1
else:
i-=1
t-=1
c += 1
fig.tight_layout() #그래프 간격 유지
E단계, 즉 결측치를 min 값으로 대체한 것을 plot 한 것이다. 마찬가지로 결측치로 비었던 부분들이 채워진것을 볼 수있다. 다른 단계들과 정확한 비교를 위해 차후 밑에서 plot했던 것들을 다 모은다음 비교하도록 하겠다.
data
| nSeq | DI | PORT4_CHN1 | PORT6_CHN1 | PORT7_CHN1 | |
|---|---|---|---|---|---|
| 0 | 0 | 0 | NaN | -0.4806 | 0.3770 |
| 1 | 1 | 0 | -0.2104 | -0.4819 | 0.3759 |
| 2 | 2 | 0 | -0.2103 | -0.4822 | NaN |
| 3 | 3 | 0 | -0.2119 | -0.4819 | 0.3806 |
| 4 | 4 | 0 | -0.2148 | -0.4812 | NaN |
| ... | ... | ... | ... | ... | ... |
| 30445 | 30445 | 0 | NaN | -0.6162 | 0.1777 |
| 30446 | 30446 | 0 | -0.0034 | -0.6157 | 0.1780 |
| 30447 | 30447 | 0 | -0.0037 | -0.6145 | 0.1772 |
| 30448 | 30448 | 0 | 0.0000 | -0.6157 | 0.1820 |
| 30449 | 30449 | 0 | 0.0005 | -0.6144 | 0.1855 |
30450 rows × 5 columns
#전체 비교를 위해 모든 데이터를 불러왔다. 이를 위해서 위 플롯과 다른 점은 data list 의 요소가 6개로 늘어 났으며, 3X2 이기 때문에 i 뿐만 아니라 j를 활용한 2차열 배열로 위치를
#조정한다. 전체적인 방식은 위와 동일하다.
column_list = [' PORT4_CHN1', ' PORT6_CHN1', ' PORT7_CHN1']
data_list = [data, Adata, Bdata, Cdata, Ddata, Edata]
title_list = ['original','dropna', 'average', 'median', 'Max', 'Min']
color_list=['royalblue','orangered','seagreen'] #컬러 리스트 세개 선언
c = 0 #color지정을 위한 변수
for column in column_list:
fig, ax = plt.subplots(3, 2, figsize=(20, 15)) #한 figure에 그래프를 6개 그리기 위해 초기화
i = 0 #plot 위치 지정을 위한 변수
j = 0 #plot 위치 지정을 위한 변수
k = 0 #title 지정을 위한 변수
for data in data_list:
ax[i][j].plot(data[column], label=column, color = color_list[c]) #해당하는 그래프 그리기
ax[i][j].legend(loc='upper right') #라벨명 위치 설정
ax[i][j].set_title(title_list[k]) #그래프 제목 설정
if(j==0):
j += 1
k += 1
else:
j -= 1
i += 1
k += 1
c += 1
fig.tight_layout() #그래프 간격 유지
각각 단계별로 plot한 것을 가지고는 어떤 부분들이 차이가 있는지 한 눈에 식별하기 쉽지않아 각 데이터별로 단계들을 모아서 한꺼번에 plot해보았다. 주석을 통해 코드를 보면 된다. 전체 적으로 결과들이 요구조건에 맞게 잘 나온걸 확인할 수 있다. 각각 어떤 데이터를 나타냈는지는 그래프 위에 original dropna average median max min으로 적어놓았다. dropna를 통해 결측값을 다 빼고 처리한 데이터가 예상했던 것처럼 다른 데이터보다 듬성등성하게 plot되어있는 것을 확인할 수있다. 평균값과 중간 값으로 대체된 두개의 단계의 데이터는 육안으로는 큰 차이를 식별하기 힘들만큼 유사한 것을 알 수있고, 그래프의 가운데 부분이 비교적 그래프의 위 아래보다 진하게 찍혀있었고, 이를 통해 평균과 중간 값이 잘 구현됐음을 알 수있다. 데이터의 특성상 평균과 중간값이 큰 차이를 보이지 않아 두 개의 차이를 식별하기 힘들었다. 이는 데이터가 만약 지금과 다르다면 평균과 중간값의 차이를 보일 수 있고, 그렇다면 그래프의 모양도 달라질 수 있다고 생각한다. max 와 min 으로 처리한 그래프는 각각 위와 아래 부분이 진한 것을 한 눈에 봐도 알 수 있었다. 이를 통해 요구조건에 맞게 잘 구현됐음을 확인할 수 있다. 각각의 단계별 그래프마다 어느 것이 좋고 어느 것이 나쁘다고 판단할 수는 없는 것 같다. 조작되지 않은 그래프의 값들만을 보고 싶다면 dropna를 통해 만든 데이터의 값을 사용하는 것이 가장 좋고, 평균값과 중앙값 같은 경우, 데이터의 양을 채우면서 데이터의 대체적인 분포를 확인할 때 좋을 것 같고, max나 min 같은 경우 한 쪽으로 치우쳤을때 어떤 결과가 나올지 확인하고 싶을때 쓰면 유용할 것이다. 이를 통해 데이터를 클렌징할때 여러가지 관점에서 처리할 수 있어야 하고, 원하는 상황에 맞추어 그에 맞는 적절한 데이터를 사용하는 것이 중요하다고 생각한다. 결과 값 추출 과정에서 get_min(data,column, index, window) 를 예를 들었을때 처음에 복사된 데이터인 Edata를 넣어 get_min(Edata,column, index, window)로 추출했었는데 , 이렇게 되면 순차적으로 진행하는 과정에서 이전의 결측치<-> 새로운 값을 대체해서 데이터에 들어간 값이 이후에 함수를 통해 값을 추출하는 과정에서 영향을 줘 결과적으로 플롯을 해보면 블럭 형태로 데이터 틀 손실이 많이 일어난 것 처럼 보였다. 이를 해결하기 위해 get_min(data,column, index, window)처럼 값 을 리턴할때 원시 데이터를 넣어주었고, 이렇게 되면 모든 인덱스가 기존 원시데이터 기준으로 min max average median 값을 계산 하기 때문에 플랏 했을 때도 모양이 데이터 손실이 크게 발생하지 않는 것처럼 나타난다.
A) Find the index with the median value for each parameter
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data = pd.read_csv("/Users/kimdaehun/Desktop/dataset/ds_hw1_dataset.csv")
data
| nSeq | DI | PORT4_CHN1 | PORT6_CHN1 | PORT7_CHN1 | |
|---|---|---|---|---|---|
| 0 | 0 | 0 | NaN | -0.4806 | 0.3770 |
| 1 | 1 | 0 | -0.2104 | -0.4819 | 0.3759 |
| 2 | 2 | 0 | -0.2103 | -0.4822 | NaN |
| 3 | 3 | 0 | -0.2119 | -0.4819 | 0.3806 |
| 4 | 4 | 0 | -0.2148 | -0.4812 | NaN |
| ... | ... | ... | ... | ... | ... |
| 30445 | 30445 | 0 | NaN | -0.6162 | 0.1777 |
| 30446 | 30446 | 0 | -0.0034 | -0.6157 | 0.1780 |
| 30447 | 30447 | 0 | -0.0037 | -0.6145 | 0.1772 |
| 30448 | 30448 | 0 | 0.0000 | -0.6157 | 0.1820 |
| 30449 | 30449 | 0 | 0.0005 | -0.6144 | 0.1855 |
30450 rows × 5 columns
part2data = data.copy()
medians ={' PORT4_CHN1':[], ' PORT6_CHN1':[], ' PORT7_CHN1':[]}# 중간값을 넣어놓는 딕셔너리
medians_index = {' PORT4_CHN1':[], ' PORT6_CHN1':[], ' PORT7_CHN1':[]}#그에 대한 인덱스를 넣어 놓는 딕셔너리
for column in [' PORT4_CHN1', ' PORT6_CHN1', ' PORT7_CHN1']: #column을 돌면서
medians[column]= part2data[column].median() #medians에 .median()을 통한 중간 값을 각 열 마다 구분해서 넣는다
print(medians)
for column in [' PORT4_CHN1', ' PORT6_CHN1', ' PORT7_CHN1']: #column을 돌면서
for index in range(len(part2data)): #데이터 길이 만큼 인덱스 탐색
if medians[column]==part2data.loc[index,column]: # 데이터 값이 medians값과 동일한 인덱스 찾기
medians_index[column].append(index)#찾으면 medians_index에 넣는다.
print(medians_index)
{' PORT4_CHN1': -0.0835, ' PORT6_CHN1': 0.18080000000000002, ' PORT7_CHN1': 0.0547}
{' PORT4_CHN1': [1844, 11729, 13147, 13631, 13690, 14880, 15049, 15761, 26545, 27011, 27634, 28200], ' PORT6_CHN1': [], ' PORT7_CHN1': [26194, 29621, 29640, 29642]}
medians, medians_index를 각각 만들어 놨으며, 각각 데이터 프레임의 열마다 median 값은 medians, 그에 대한 인덱스는 medians_index에 들어간다. 딕셔너리 형태로 만들어 놓았다. 코드 설명은 주석을 통해 확인하면 된다. 결과만 놓고 봤을때, 각 열마다의 중간 값은 잘 나오고, 그 값을 가지는 인덱스는 port4에서는 12개 port7에서는 4개가 나왔는데, port6에서는 하나도 없는 점을 확인할 수 있다.
def find_nearest(array, value):
b1=0
c1=[]
for i in range(len(part2data)):
b1= abs(value-array[i])
c1.append(b1)
d1= pd.DataFrame(c1, columns=['b1'])
answer= np.min(d1)
return np.where(d1 == answer)[0]
ffn6=find_nearest(data.loc[:,' PORT6_CHN1'],medians[' PORT6_CHN1'])
for i in range (len(ffn6)):
medians_index[' PORT6_CHN1'].append(ffn6[i])
print( medians_index[' PORT6_CHN1'])
[1483, 5608, 15535]
/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/numpy/core/fromnumeric.py:84: FutureWarning: In a future version, DataFrame.min(axis=None) will return a scalar min over the entire DataFrame. To retain the old behavior, use 'frame.min(axis=0)' or just 'frame.min()' return reduction(axis=axis, out=out, **passkwargs)
따라서 내 기준을 임의로 만들었는데, 이 코드는 part2-2 를 해결하고 난 이후에 그 기준을 그대로 적었다. 코드 설명은 차후 파트 2-2에서 진행하겠다. 간단하게 설명하자면 중간값과 겹치는 데이터 값을 가진 인덱스가 port6에는 존재하지 않아, 이와 가장 가까운 값을 가지는 인덱스를 찾는 함수를 구현했고, 여기서 나온 값들을 medians_index['port6_CHN1']에 첨부하여 딕셔너리를 완성햇다.
print(medians_index)
{' PORT4_CHN1': [1844, 11729, 13147, 13631, 13690, 14880, 15049, 15761, 26545, 27011, 27634, 28200], ' PORT6_CHN1': [1483, 5608, 15535], ' PORT7_CHN1': [26194, 29621, 29640, 29642]}
완성된 딕셔너리를 프린트했다
B) Extract data as much as 15% of the total data length back and forth around the index of A) for each parameter.
for column in [' PORT4_CHN1', ' PORT6_CHN1', ' PORT7_CHN1']: #column을 돌면서
print(medians_index[column])
[1844, 11729, 13147, 13631, 13690, 14880, 15049, 15761, 26545, 27011, 27634, 28200] [1483, 5608, 15535] [26194, 29621, 29640, 29642]
열을 각각 돌면서 열마다 인덱스 값을 추출했다
A=len(part2data)*0.15
length= int(len(part2data)*0.15) #전체 데이터의 15%길이 측정
parsed_data=[] #리스트 생성
for column in [' PORT4_CHN1',' PORT6_CHN1',' PORT7_CHN1']:
for i_ndex in medians_index[column]:# medians_index안에 있는 요소들을 탐색
if i_ndex-length<0: #평균값을 가진 인덱스-length가 0보다 작을경우,
parsed_data.append(pd.DataFrame(part2data.loc[0:i_ndex+length][column]))#전체 데이터 0부터 요소+length까지 그룹화 후 리스트에 추가
elif i_ndex+length>len(part2data):# 평균값을 가진 익덱스+length가 전체 데이터 마지막 index보다 클경우
parsed_data.append(pd.DataFrame(part2data.loc[i_ndex-length:len(part2data)][column]))#요소-length부터 전체 데이터 마지막 까지 그룹화 후 리스트에 추가
else: #둘 다 아니면
parsed_data.append(pd.DataFrame(part2data.loc[i_ndex-length:i_ndex+length][column]))#요소-length 부터 요소 +length까지 그룹화 후 리스트 추가
parsed_data
[ PORT4_CHN1
0 NaN
1 -0.2104
2 -0.2103
3 -0.2119
4 -0.2148
... ...
6407 -1.7621
6408 -1.7534
6409 -1.7402
6410 NaN
6411 -1.7144
[6412 rows x 1 columns],
PORT4_CHN1
7162 -0.0095
7163 NaN
7164 NaN
7165 -0.0132
7166 -0.0156
... ...
16292 -0.1226
16293 -0.1199
16294 -0.1157
16295 -0.1133
16296 -0.1094
[9135 rows x 1 columns],
PORT4_CHN1
8580 0.3889
8581 0.3939
8582 NaN
8583 0.3997
8584 0.4016
... ...
17710 1.2954
17711 NaN
17712 1.3247
17713 1.3406
17714 1.3555
[9135 rows x 1 columns],
PORT4_CHN1
9064 0.0846
9065 NaN
9066 0.0400
9067 0.0156
9068 -0.0059
... ...
18194 -0.3979
18195 -0.3760
18196 -0.3530
18197 -0.3325
18198 -0.3105
[9135 rows x 1 columns],
PORT4_CHN1
9123 -0.3406
9124 -0.3379
9125 -0.3379
9126 -0.3340
9127 NaN
... ...
18253 0.5410
18254 0.5549
18255 0.5696
18256 NaN
18257 NaN
[9135 rows x 1 columns],
PORT4_CHN1
10313 NaN
10314 NaN
10315 -0.1440
10316 NaN
10317 NaN
... ...
19443 1.5165
19444 1.5520
19445 NaN
19446 1.6145
19447 1.6470
[9135 rows x 1 columns],
PORT4_CHN1
10482 -0.5093
10483 NaN
10484 NaN
10485 -0.4897
10486 -0.4829
... ...
19612 NaN
19613 -0.0474
19614 -0.0210
19615 0.0034
19616 0.0273
[9135 rows x 1 columns],
PORT4_CHN1
11194 -0.3633
11195 NaN
11196 -0.3743
11197 NaN
11198 -0.3862
... ...
20324 0.9766
20325 1.0161
20326 1.0547
20327 1.0942
20328 1.1331
[9135 rows x 1 columns],
PORT4_CHN1
21978 NaN
21979 NaN
21980 NaN
21981 0.2268
21982 0.1907
... ...
30445 NaN
30446 -0.0034
30447 -0.0037
30448 0.0000
30449 0.0005
[8472 rows x 1 columns],
PORT4_CHN1
22444 -0.3247
22445 NaN
22446 -0.3682
22447 -0.3906
22448 -0.4150
... ...
30445 NaN
30446 -0.0034
30447 -0.0037
30448 0.0000
30449 0.0005
[8006 rows x 1 columns],
PORT4_CHN1
23067 0.9336
23068 NaN
23069 0.8384
23070 0.7896
23071 0.7439
... ...
30445 NaN
30446 -0.0034
30447 -0.0037
30448 0.0000
30449 0.0005
[7383 rows x 1 columns],
PORT4_CHN1
23633 -0.2832
23634 NaN
23635 NaN
23636 -0.1689
23637 -0.1289
... ...
30445 NaN
30446 -0.0034
30447 -0.0037
30448 0.0000
30449 0.0005
[6817 rows x 1 columns],
PORT6_CHN1
0 -0.4806
1 -0.4819
2 -0.4822
3 -0.4819
4 -0.4812
... ...
6046 0.0054
6047 0.0225
6048 0.0427
6049 0.0634
6050 0.0842
[6051 rows x 1 columns],
PORT6_CHN1
1041 -0.2878
1042 -0.2874
1043 -0.2869
1044 -0.2880
1045 -0.2919
... ...
10171 -0.4087
10172 -0.4121
10173 -0.4137
10174 -0.4165
10175 -0.4160
[9135 rows x 1 columns],
PORT6_CHN1
10968 0.2256
10969 0.2314
10970 0.2378
10971 0.2406
10972 NaN
... ...
20098 1.4255
20099 1.3997
20100 1.3705
20101 1.3386
20102 1.3075
[9135 rows x 1 columns],
PORT7_CHN1
21627 NaN
21628 -0.9249
21629 -0.8704
21630 -0.8127
21631 -0.7584
... ...
30445 0.1777
30446 0.1780
30447 0.1772
30448 0.1820
30449 0.1855
[8823 rows x 1 columns],
PORT7_CHN1
25054 -1.0786
25055 NaN
25056 -1.1108
25057 -1.1245
25058 -1.1351
... ...
30445 0.1777
30446 0.1780
30447 0.1772
30448 0.1820
30449 0.1855
[5396 rows x 1 columns],
PORT7_CHN1
25073 -1.2881
25074 -1.3062
25075 NaN
25076 -1.3475
25077 -1.3705
... ...
30445 0.1777
30446 0.1780
30447 0.1772
30448 0.1820
30449 0.1855
[5377 rows x 1 columns],
PORT7_CHN1
25075 NaN
25076 -1.3475
25077 -1.3705
25078 -1.3928
25079 -1.4143
... ...
30445 0.1777
30446 0.1780
30447 0.1772
30448 0.1820
30449 0.1855
[5375 rows x 1 columns]]
먼저 전체 데이터에서 15% 길이를 추출하기 위해 A에 식을 대입해서 길이를 알아내었고, 소수점으로 값이 나와 int형으로 반환하여 소수점을 버렸다. 그 후 parsed_data라는 리스트를 하나 생성했고, medians_index를 각 포트마다 돌면서, medians_index안에있는 요소의 값에 length를 빼서, 0보다 작을 경우 전체 데이터에서 제일 처음 인덱스부터, 확인하는 인덱스 기준+length만큼 한 그룹이 지어지게 된다. 이를 데이터 프레임화 시켜 parsed_data 리스트 안에 추가하였다. 만약 medians_index안에 있는 요소+length가 데이터 크기보다 크다면, 요소-length부터 전체데이터의 마지막 행 값까지 그룹을 지어주면된다. 그리고 이렇게 그룹을 지어준 것을 parsed_data 리스트 안에 추가하였고, 위의 두조건에 해당하지 않을 경우는 그냥 medians_index 안에 있는 요소-length 부터 요소+length까지 그룹화를 시켜 parsed_data리스트 안에 넣어주면된다.
C) Organize the result data to data type “dict” with Key and Value
parse_to_dict = [] #딕셔너리화할 파싱 데이터를 저장할 리스트
for index in range(len(parsed_data)): #column을 돌며
parse_to_dict.append(parsed_data[index].to_dict()) #딕셔너리화
parse_to_dict #딕셔너리화한 파싱 데이터 확인
[{' PORT4_CHN1': {0: nan,
1: -0.2104,
2: -0.2103,
3: -0.2119,
4: -0.2148,
5: -0.2156,
6: nan,
7: nan,
8: -0.2209,
9: -0.2227,
10: -0.2253,
11: nan,
12: -0.2278,
13: nan,
14: nan,
15: -0.2322,
16: nan,
17: -0.2349,
18: nan,
19: nan,
20: nan,
21: nan,
22: -0.2374,
23: -0.2383,
24: -0.24,
25: nan,
26: nan,
27: -0.2393,
28: -0.2397,
29: nan,
30: -0.2378,
31: -0.2373,
32: nan,
33: nan,
34: -0.2358,
35: nan,
36: -0.2373,
37: nan,
38: -0.2339,
39: nan,
40: -0.2311,
41: -0.23,
42: -0.23,
43: -0.2285,
44: -0.2261,
45: nan,
46: -0.2231,
47: -0.2227,
48: nan,
49: -0.2202,
50: -0.2183,
51: nan,
52: -0.2158,
53: -0.2144,
54: -0.2119,
55: nan,
56: nan,
57: nan,
58: -0.2056,
59: -0.2056,
60: -0.2021,
61: -0.2021,
62: -0.2,
63: -0.1989999999999999,
64: -0.1973,
65: nan,
66: -0.1947,
67: nan,
68: nan,
69: -0.1875,
70: nan,
71: nan,
72: -0.186,
73: nan,
74: -0.1843,
75: -0.1831,
76: nan,
77: -0.1821,
78: -0.1814,
79: -0.1786,
80: -0.1787,
81: -0.1763,
82: nan,
83: nan,
84: nan,
85: -0.1729,
86: nan,
87: -0.1733,
88: -0.1738,
89: -0.1748,
90: -0.1743,
91: nan,
92: -0.1747,
93: nan,
94: nan,
95: nan,
96: -0.1755,
97: nan,
98: nan,
99: -0.1758,
100: -0.1763,
101: nan,
102: nan,
103: -0.1787,
104: -0.1787,
105: -0.1777,
106: nan,
107: -0.1807,
108: -0.1807,
109: nan,
110: -0.1826,
111: -0.1841,
112: -0.1843,
113: nan,
114: -0.1879,
115: -0.1899,
116: -0.1889999999999999,
117: nan,
118: -0.1914,
119: -0.1924,
120: -0.1924,
121: -0.1952,
122: -0.1962,
123: nan,
124: nan,
125: -0.2002,
126: -0.2017,
127: nan,
128: nan,
129: nan,
130: -0.2083,
131: -0.21,
132: nan,
133: nan,
134: nan,
135: nan,
136: -0.2156,
137: -0.2173,
138: -0.2188,
139: nan,
140: nan,
141: nan,
142: -0.2273,
143: nan,
144: -0.2283,
145: -0.2302,
146: nan,
147: -0.2314,
148: -0.2344,
149: -0.2358,
150: nan,
151: -0.2393,
152: -0.2432,
153: nan,
154: -0.2416,
155: -0.2441,
156: nan,
157: nan,
158: -0.2451,
159: -0.2466,
160: -0.2473,
161: -0.2478,
162: -0.2505,
163: -0.2485,
164: nan,
165: nan,
166: -0.2493,
167: nan,
168: nan,
169: -0.251,
170: -0.2505,
171: -0.252,
172: -0.252,
173: nan,
174: -0.252,
175: -0.25,
176: -0.2493,
177: nan,
178: -0.248,
179: -0.2485,
180: nan,
181: nan,
182: -0.2448,
183: -0.2437,
184: -0.2417,
185: -0.2405,
186: -0.2393,
187: -0.2383,
188: -0.2373,
189: nan,
190: -0.2344,
191: nan,
192: -0.2324,
193: nan,
194: -0.2289999999999999,
195: -0.2251,
196: -0.2253,
197: nan,
198: -0.2202,
199: -0.2183,
200: nan,
201: -0.2131,
202: -0.2122,
203: -0.2087,
204: -0.2068,
205: -0.2036,
206: -0.2023,
207: -0.2019,
208: -0.1992,
209: -0.1963,
210: nan,
211: -0.1931,
212: nan,
213: nan,
214: nan,
215: -0.1846,
216: -0.1836,
217: nan,
218: -0.1807,
219: nan,
220: -0.1775,
221: -0.1772,
222: -0.1763,
223: nan,
224: nan,
225: -0.1731,
226: nan,
227: nan,
228: -0.1704,
229: nan,
230: -0.1697,
231: -0.168,
232: nan,
233: -0.168,
234: -0.1685,
235: -0.1678,
236: -0.1678,
237: -0.1689,
238: nan,
239: nan,
240: nan,
241: nan,
242: -0.1709,
243: -0.1719,
244: -0.1699,
245: -0.1724,
246: -0.1736,
247: nan,
248: -0.1716,
249: -0.1727,
250: -0.1733,
251: -0.1748,
252: nan,
253: -0.1763,
254: -0.1782,
255: nan,
256: -0.1792,
257: -0.1794,
258: -0.1797,
259: nan,
260: -0.1801,
261: -0.1826,
262: -0.1836,
263: -0.1846,
264: -0.1855,
265: -0.1863,
266: nan,
267: -0.1885,
268: nan,
269: -0.1929,
270: -0.1934,
271: -0.1948,
272: -0.1958,
273: -0.1978,
274: nan,
275: nan,
276: -0.1992,
277: -0.2002,
278: -0.2002,
279: -0.2007,
280: -0.2021,
281: -0.2021,
282: -0.2031,
283: -0.2041,
284: -0.2031,
285: -0.2024,
286: -0.2039,
287: nan,
288: -0.207,
289: -0.207,
290: nan,
291: -0.2068,
292: nan,
293: -0.209,
294: nan,
295: -0.207,
296: -0.2068,
297: nan,
298: -0.208,
299: -0.209,
300: nan,
301: -0.2075,
302: -0.2083,
303: -0.2079,
304: nan,
305: -0.2075,
306: nan,
307: -0.2095,
308: -0.2109,
309: -0.2104,
310: -0.2104,
311: nan,
312: -0.2124,
313: -0.2119,
314: -0.2107,
315: -0.2104,
316: -0.2103,
317: nan,
318: -0.209,
319: -0.2075,
320: nan,
321: nan,
322: -0.2083,
323: -0.2085,
324: nan,
325: -0.2065,
326: -0.207,
327: nan,
328: -0.2061,
329: nan,
330: nan,
331: -0.2048,
332: -0.207,
333: -0.2039,
334: -0.2041,
335: nan,
336: -0.2034,
337: -0.2041,
338: nan,
339: -0.2039,
340: -0.2039,
341: -0.2021,
342: nan,
343: nan,
344: -0.2014,
345: nan,
346: nan,
347: -0.1987,
348: -0.1973,
349: -0.1963,
350: nan,
351: -0.1958,
352: nan,
353: -0.1963,
354: nan,
355: -0.1958,
356: -0.1943,
357: nan,
358: -0.1936,
359: -0.1943,
360: nan,
361: nan,
362: -0.1929,
363: nan,
364: -0.1909,
365: nan,
366: -0.1919,
367: nan,
368: nan,
369: -0.1904,
370: -0.1919,
371: nan,
372: -0.1892,
373: nan,
374: -0.1892,
375: -0.1897,
376: -0.1887,
377: -0.1892,
378: nan,
379: -0.1895,
380: nan,
381: -0.1892,
382: -0.1887,
383: -0.1889999999999999,
384: -0.1875,
385: -0.1866,
386: -0.1865,
387: -0.1865,
388: -0.1846,
389: -0.1836,
390: nan,
391: -0.1812,
392: nan,
393: nan,
394: nan,
395: -0.1777,
396: -0.1775,
397: nan,
398: -0.1753,
399: nan,
400: nan,
401: -0.1746,
402: -0.1753,
403: -0.1729,
404: -0.1748,
405: -0.1748,
406: -0.1725,
407: -0.1719,
408: nan,
409: -0.1704,
410: -0.1699,
411: -0.1704,
412: -0.1699,
413: nan,
414: -0.1714,
415: -0.1704,
416: nan,
417: nan,
418: nan,
419: -0.1689,
420: -0.1689,
421: nan,
422: nan,
423: -0.1658,
424: -0.1665,
425: nan,
426: nan,
427: -0.1682,
428: -0.1685,
429: nan,
430: -0.168,
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25543: nan,
25544: -1.9978,
25545: -1.9602,
25546: -1.9203,
25547: nan,
25548: -1.8329,
25549: -1.7864,
25550: nan,
25551: -1.696,
25552: -1.6442,
25553: -1.5898,
25554: nan,
25555: nan,
25556: -1.408,
25557: -1.3539,
25558: -1.3004,
25559: -1.2498,
25560: -1.198,
25561: -1.144,
25562: -1.0872,
25563: -1.0312,
25564: nan,
25565: -0.9191,
25566: -0.8618,
25567: -0.8079,
25568: nan,
25569: nan,
25570: -0.6475,
25571: -0.5959,
25572: -0.546,
25573: -0.4956,
25574: nan,
25575: -0.3984,
25576: nan,
25577: nan,
25578: -0.2621,
25579: -0.2192,
25580: -0.1763,
25581: -0.1354,
25582: -0.0938,
25583: nan,
25584: -0.0186,
25585: 0.0181,
25586: 0.0531,
25587: 0.0884,
25588: 0.1233,
25589: 0.1541,
25590: 0.1877,
25591: 0.2189999999999999,
25592: nan,
25593: 0.2791,
25594: 0.3076,
25595: 0.3353,
25596: 0.3662,
25597: 0.3949,
25598: 0.4204,
25599: 0.4436,
25600: 0.4692,
25601: 0.4946,
25602: 0.5173,
25603: 0.5393,
25604: 0.5634,
25605: 0.5834,
25606: nan,
25607: 0.621,
25608: 0.6377,
25609: 0.6556,
25610: 0.6727,
25611: 0.6913,
25612: 0.7062,
25613: nan,
25614: 0.7422,
25615: 0.76,
25616: 0.7765,
25617: nan,
25618: 0.8075,
25619: 0.821,
25620: 0.8342,
25621: 0.8474,
25622: 0.8602,
25623: 0.8735,
25624: 0.8857,
25625: 0.9006,
25626: 0.9143,
25627: nan,
25628: 0.9381,
25629: 0.9495,
25630: 0.9575,
25631: 0.9653,
25632: nan,
25633: 0.9819,
25634: 0.9895,
25635: nan,
25636: 1.0039,
25637: 1.0095,
25638: nan,
25639: 1.0165,
25640: 1.0146,
25641: nan,
25642: 1.0171,
25643: nan,
25644: nan,
25645: nan,
25646: 1.0074,
25647: nan,
25648: 0.9873,
25649: 0.9741,
25650: 0.9592,
25651: 0.9426,
25652: 0.9236,
25653: 0.9053,
25654: 0.8811,
25655: 0.8572,
25656: 0.8290000000000001,
25657: nan,
25658: 0.7727,
25659: 0.7402,
25660: 0.7081,
25661: 0.6719,
25662: 0.64,
25663: 0.605,
25664: 0.5669,
25665: 0.5302,
25666: 0.49,
25667: 0.4509,
25668: 0.4097,
25669: 0.3689,
25670: 0.3271,
25671: 0.2883,
25672: 0.2466,
25673: 0.207,
25674: 0.1667,
25675: 0.1272,
25676: 0.0894,
25677: 0.0476,
25678: 0.0082,
25679: -0.03,
25680: -0.069,
25681: -0.1046,
25682: -0.1439,
25683: nan,
25684: -0.2166,
25685: -0.2551,
25686: nan,
25687: -0.324,
25688: nan,
25689: -0.3906,
25690: -0.4253,
25691: -0.4589999999999999,
25692: nan,
25693: -0.5239,
25694: -0.5566,
25695: -0.5884,
25696: nan,
25697: nan,
25698: -0.6694,
25699: nan,
25700: -0.72,
25701: nan,
25702: -0.7611,
25703: -0.7808,
25704: nan,
25705: -0.8165,
25706: -0.8335,
25707: -0.8496,
25708: -0.8663,
25709: -0.8862,
25710: -0.9235,
25711: nan,
25712: -0.9835,
25713: -1.0088,
25714: nan,
25715: nan,
25716: nan,
25717: -1.0903,
25718: nan,
25719: -1.1277,
25720: -1.1455,
25721: -1.1635,
25722: -1.1807,
25723: nan,
25724: -1.2202,
25725: nan,
25726: -1.2535,
25727: -1.2733,
25728: -1.2927,
25729: nan,
25730: nan,
25731: -1.3533,
25732: -1.3738,
25733: -1.3899,
25734: -1.402,
25735: -1.4187,
25736: -1.4412,
25737: -1.4569,
25738: -1.4701,
25739: -1.4858,
25740: -1.4969,
25741: -1.5071,
25742: -1.5186,
25743: nan,
25744: -1.533,
25745: -1.5371,
25746: nan,
25747: -1.5345,
25748: -1.5325,
25749: -1.5226,
25750: nan,
25751: -1.5,
25752: -1.4875,
25753: nan,
25754: -1.4548,
25755: -1.4366,
25756: -1.4122,
25757: -1.3872,
25758: -1.3574,
25759: nan,
25760: nan,
25761: -1.2446,
25762: -1.2109,
25763: -1.178,
25764: -1.1475,
25765: -1.115,
25766: -1.0822,
25767: -1.0479,
25768: -1.0112,
25769: nan,
25770: -0.9324,
25771: -0.8928,
25772: -0.8517,
25773: -0.8096,
25774: -0.7654,
25775: -0.7207,
25776: -0.6758,
25777: -0.6335,
25778: nan,
25779: -0.5475,
25780: nan,
25781: -0.4658,
25782: nan,
25783: -0.3821,
25784: -0.3396,
25785: nan,
25786: nan,
25787: -0.228,
25788: -0.1912,
25789: -0.1558,
25790: -0.1206,
25791: -0.0865,
25792: -0.0552,
25793: nan,
25794: 0.0079,
25795: 0.0393,
25796: nan,
25797: 0.0941,
25798: 0.1199,
25799: nan,
25800: 0.1669999999999999,
25801: nan,
25802: 0.2148,
25803: 0.2366,
25804: 0.2563,
25805: 0.2742,
25806: 0.2969,
25807: 0.3132,
25808: 0.3281,
25809: 0.345,
25810: 0.3582,
25811: 0.3708,
25812: nan,
25813: 0.3958,
25814: 0.4099,
25815: 0.4192,
25816: 0.426,
25817: 0.4368,
25818: nan,
25819: nan,
25820: 0.4563,
25821: 0.4642,
25822: nan,
25823: 0.4724,
25824: 0.4755,
25825: 0.4785,
25826: 0.478,
25827: 0.4775,
25828: nan,
25829: 0.4805,
25830: 0.4771,
25831: nan,
25832: 0.4705,
25833: 0.4663,
25834: 0.4612,
25835: nan,
25836: 0.4521,
25837: 0.4426,
25838: 0.4341,
25839: 0.4227,
25840: 0.4149,
25841: nan,
25842: 0.3944,
25843: 0.3843,
25844: nan,
25845: 0.3555,
25846: 0.3396,
25847: 0.3242,
25848: 0.3069,
25849: 0.2871,
25850: 0.2659,
25851: nan,
25852: 0.2197,
25853: 0.1987,
25854: 0.1775,
25855: 0.1528,
25856: 0.1259,
25857: 0.0997,
25858: 0.0732,
25859: 0.0437,
25860: 0.0165,
25861: -0.011,
25862: -0.0408,
25863: -0.0681,
25864: -0.0962,
25865: -0.1289,
25866: -0.1616,
25867: nan,
25868: -0.2262,
25869: -0.2612,
25870: -0.296,
25871: -0.3296,
25872: -0.3647,
25873: -0.3976,
25874: -0.4336,
25875: -0.4646,
25876: -0.4991,
25877: -0.5326,
25878: -0.5654,
25879: -0.5986,
25880: -0.6313,
25881: -0.6609,
25882: nan,
25883: -0.7173,
25884: -0.7437,
25885: -0.7693,
25886: -0.7947,
25887: nan,
25888: nan,
25889: -0.882,
25890: -0.9158,
25891: -0.9468,
25892: -0.9724,
25893: -0.9956,
25894: -1.0188,
25895: -1.0397,
25896: nan,
25897: -1.0786,
25898: -1.0907,
25899: nan,
25900: -1.1155,
25901: -1.1256,
25902: nan,
25903: -1.1448,
25904: nan,
25905: -1.1536,
25906: -1.1638,
25907: -1.1716,
25908: -1.1785,
25909: -1.1801,
25910: nan,
25911: -1.1924,
25912: -1.1897,
25913: -1.1797,
25914: -1.1636,
25915: nan,
25916: nan,
25917: -1.1932,
25918: -1.1907,
25919: -1.1882,
25920: -1.1866,
25921: -1.1807,
25922: nan,
25923: nan,
25924: -1.1735,
25925: -1.156,
25926: -1.1414,
25927: -1.1292,
25928: -1.1177,
25929: -1.1053,
25930: -1.095,
25931: nan,
25932: -1.0796,
25933: -1.0702,
25934: -1.0635,
25935: -1.0547,
25936: -1.0498,
25937: nan,
25938: -1.0334,
25939: -1.0249,
25940: -1.0166,
25941: -1.0057,
25942: -0.995,
25943: -0.9841,
25944: nan,
25945: -0.9565,
25946: -0.9382,
25947: -0.9192,
25948: -0.9011,
25949: -0.8805,
25950: -0.8579,
25951: -0.8365,
25952: nan,
25953: nan,
25954: -0.7594,
25955: -0.7306,
25956: nan,
25957: -0.6697,
25958: nan,
25959: -0.6047,
25960: -0.5701,
25961: -0.5371,
25962: -0.5023,
25963: -0.4663,
25964: -0.428,
25965: -0.3922,
25966: -0.3556,
25967: -0.3188,
25968: -0.2816,
25969: -0.2451,
25970: nan,
25971: -0.1724,
25972: -0.1377,
25973: nan,
25974: -0.0637,
25975: -0.0286,
25976: nan,
25977: 0.0442,
25978: 0.0784,
25979: 0.1102,
25980: 0.1433,
25981: 0.1757,
25982: 0.2087,
25983: 0.24,
25984: 0.2689999999999999,
25985: 0.3015,
25986: 0.3297,
25987: 0.3574,
25988: 0.3856,
25989: 0.4113,
25990: 0.4341,
25991: 0.4602,
25992: 0.4849,
25993: 0.5024,
25994: 0.5251,
25995: nan,
25996: 0.5659,
25997: 0.5817,
25998: nan,
25999: 0.6157,
26000: 0.6259,
26001: 0.6389,
26002: 0.6494,
26003: 0.6582,
26004: nan,
26005: 0.6731,
26006: 0.6791,
26007: 0.6812,
26008: nan,
26009: 0.6841,
26010: 0.6862,
26011: nan,
26012: 0.6816,
26013: 0.6792,
26014: 0.6729999999999999,
26015: 0.6702,
26016: 0.6649,
26017: 0.6587,
26018: 0.6516,
26019: 0.6431,
26020: nan,
26021: 0.6235,
26022: 0.6117,
26023: 0.5984,
26024: 0.5878,
26025: 0.5725,
26026: nan,
26027: nan,
26028: 0.5206,
26029: 0.5023,
26030: 0.4819,
26031: 0.4602,
26032: 0.4424,
26033: 0.4213,
26034: 0.3984,
26035: 0.3774,
26036: nan,
26037: nan,
26038: nan,
26039: nan,
26040: 0.2522,
26041: 0.223,
26042: 0.1975,
26043: 0.1687,
26044: nan,
26045: 0.1062,
26046: nan,
26047: 0.0371,
26048: nan,
26049: -0.0309,
26050: -0.066,
26051: -0.0999,
26052: -0.132,
26053: -0.1677,
26054: -0.2023,
26055: nan,
26056: -0.2739,
26057: -0.3116,
26058: nan,
26059: -0.3826,
26060: -0.4194,
26061: nan,
26062: nan,
26063: -0.5262,
26064: -0.5608,
26065: -0.5933,
26066: -0.6252,
26067: -0.6597,
26068: -0.6924,
26069: -0.7224,
26070: nan,
26071: nan,
26072: nan,
26073: -0.8411,
26074: nan,
...}}]
parsed_data를 하나씩 돌며, 이를 to_dict()를 통해 딕셔너리화 해서 parse_to_dict라는 리스트에 저장시킨다.
D) Save the result as a json file.
#json.dump
import json
dict =(parse_to_dict)
with open('./data.json','w')as f:
json.dump(dict,f,ensure_ascii=False,indent=4)
parse_to _dict 내용이 data.json파일로 디렉토리에 저장된다.
A) Find the index with the average value for each parameter
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data = pd.read_csv("/Users/kimdaehun/Desktop/dataset/ds_hw1_dataset.csv")
part2data = data.copy()
averages ={' PORT4_CHN1':[], ' PORT6_CHN1':[], ' PORT7_CHN1':[]}#평균값을 담을 딕셔너리 생성
averages_index = {' PORT4_CHN1':[], ' PORT6_CHN1':[], ' PORT7_CHN1':[]}#인덱스를 담을 딕셔너리 생성
for column in [' PORT4_CHN1', ' PORT6_CHN1', ' PORT7_CHN1']: #column을 돌면서
averages[column]= part2data[column].mean() #열마다 평균값을 averages에 열마다 분류해 담는다
print(averages)
for column in [' PORT4_CHN1', ' PORT6_CHN1', ' PORT7_CHN1']: #column을 돌면서
for index in range(len(part2data)):
if averages[column]==part2data.loc[index,column]:#데이터의 값이 평균과 같다면
averages_index[column].append(index) # aveages_index에 저장
print(averages_index)
{' PORT4_CHN1': -0.000945556157368655, ' PORT6_CHN1': 0.42242629272885285, ' PORT7_CHN1': -0.0670108230226764}
{' PORT4_CHN1': [], ' PORT6_CHN1': [], ' PORT7_CHN1': []}
파트 2-1과 같은 방식으로 먼저 진행해봤는데, 인덱스가 하나도 나오지 않는다 port 4 6 7 모두 임의의 기준을 가지고 모델을 만들어야 한다.
part2data = data.copy()
averages ={' PORT4_CHN1':[], ' PORT6_CHN1':[], ' PORT7_CHN1':[]}
averages_index = {' PORT4_CHN1':[], ' PORT6_CHN1':[], ' PORT7_CHN1':[]}
for column in [' PORT4_CHN1', ' PORT6_CHN1', ' PORT7_CHN1']: #column을 돌면서
averages[column]= part2data[column].mean()
print(averages)
{' PORT4_CHN1': -0.000945556157368655, ' PORT6_CHN1': 0.42242629272885285, ' PORT7_CHN1': -0.0670108230226764}
일단 다시 평균값이 무엇인지 작성해놓는다.
averages_index = {' PORT4_CHN1':[], ' PORT6_CHN1':[], ' PORT7_CHN1':[]}
def find_nearest(array, value):# 함수 생성
b=0#초기화
c=[]# 빈 리스트 생성
for i in range(len(part2data)):#데이터 길이를 돌며
b= abs(value-array[i]) #각 열의 평균과-데이터의 값을 뺸 후 절댓값 씌운다
c.append(b) # c라는 빈 리스트에 값들을 집어 넣는다
d= pd.DataFrame(c, columns=['b'])# c를 데이터 프레임화 한다.
answer= np.min(d)# 데이터 값들중 가장 작은 값을 추출한다
return np.where(d == answer)[0]# 이 값을 가진 인덱스를 추출한다.
fn4=find_nearest(data.loc[:,' PORT4_CHN1'],averages[' PORT4_CHN1'])
fn6=find_nearest(data.loc[:,' PORT6_CHN1'],averages[' PORT6_CHN1'])
fn7=find_nearest(data.loc[:,' PORT7_CHN1'],averages[' PORT7_CHN1'])
#각 각 열마다 함수를 통해 인덱스 추출
for i in range (len(fn4)):
averages_index[' PORT4_CHN1'].append(fn4[i])
print( averages_index[' PORT4_CHN1'])
for i in range (len(fn6)):
averages_index[' PORT6_CHN1'].append(fn6[i])
print( averages_index[' PORT6_CHN1'])
for i in range (len(fn7)):
averages_index[' PORT7_CHN1'].append(fn7[i])
print( averages_index[' PORT7_CHN1'])
averages_index
#그 후 averages_index에 열마다 분리해서 나온 인덱스 값들을 넣는다.
[3597, 9658, 13531, 14810, 18797, 19178, 19716] [2512, 7122, 28057] [21759, 27650]
/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/numpy/core/fromnumeric.py:84: FutureWarning: In a future version, DataFrame.min(axis=None) will return a scalar min over the entire DataFrame. To retain the old behavior, use 'frame.min(axis=0)' or just 'frame.min()' return reduction(axis=axis, out=out, **passkwargs) /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/numpy/core/fromnumeric.py:84: FutureWarning: In a future version, DataFrame.min(axis=None) will return a scalar min over the entire DataFrame. To retain the old behavior, use 'frame.min(axis=0)' or just 'frame.min()' return reduction(axis=axis, out=out, **passkwargs) /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/numpy/core/fromnumeric.py:84: FutureWarning: In a future version, DataFrame.min(axis=None) will return a scalar min over the entire DataFrame. To retain the old behavior, use 'frame.min(axis=0)' or just 'frame.min()' return reduction(axis=axis, out=out, **passkwargs)
{' PORT4_CHN1': [3597, 9658, 13531, 14810, 18797, 19178, 19716],
' PORT6_CHN1': [2512, 7122, 28057],
' PORT7_CHN1': [21759, 27650]}
이를 해결하기 위해 나는 평균 값과 가장 가까운 값을 가지는 데이터의 인덱스를 뽑아내기로 설정했다. 이를 위해 그 인덱스를 뽑아내는 함수를 만들었는데, 원리는 평균-데이터 값에 절댓값 을 했을때 그 중에서 가장 차이가 적은 값을 뽑고, 이 값이 위치한 인덱스를 리턴하는 것이다. 코드 과정 중에 c 의 값을 왜 데이터 프레임화 시켜 d로 만들었는지 의문이 있을 수 있다. 이 이유는 결측치 때문인데, 평균-결측치 된 값들도 c 안에 들어있을 것이다. c를 min()해서 최솟값을 찾으면 nan, 즉 결측치를 제외하지 않고 최솟값을 찾으려고 해 nan을 가진 인덱스가 반환된다. 하지만 데이터 프레임화 시켜서 min()을 해보면 part1에서 해봤을때 알 수 있었지만, 결측치는 고려하지 않고 존재하는 값중에서 min값을 찾게 된다. 그렇기 때문에 다음과 같이 설정했다.
B) Extract data as much as 15% of the total data length back and forth around the index of A) for each parameter.
A=len(part2data)*0.15
length= int(len(part2data)*0.15)
parsed_data2=[]
for column in [' PORT4_CHN1',' PORT6_CHN1',' PORT7_CHN1']:
for i_ndex in averages_index[column]:
if i_ndex-length<0:
parsed_data2.append(pd.DataFrame(part2data.loc[0:i_ndex+length][column]))
elif i_ndex+length>len(part2data):
parsed_data2.append(pd.DataFrame(part2data.loc[i_ndex-length:len(part2data)][column]))
else:
parsed_data2.append(pd.DataFrame(part2data.loc[i_ndex-length:i_ndex+length][column]))
parsed_data2
[ PORT4_CHN1
0 NaN
1 -0.2104
2 -0.2103
3 -0.2119
4 -0.2148
... ...
8160 -0.1866
8161 -0.2212
8162 NaN
8163 NaN
8164 NaN
[8165 rows x 1 columns],
PORT4_CHN1
5091 0.4326
5092 0.4290
5093 0.4302
5094 0.4265
5095 0.4236
... ...
14221 NaN
14222 0.3027
14223 NaN
14224 0.2261
14225 0.1865
[9135 rows x 1 columns],
PORT4_CHN1
8964 -0.2009
8965 NaN
8966 -0.1982
8967 NaN
8968 -0.1912
... ...
18094 -0.3237
18095 NaN
18096 -0.3892
18097 NaN
18098 NaN
[9135 rows x 1 columns],
PORT4_CHN1
10243 -0.4714
10244 NaN
10245 NaN
10246 NaN
10247 -0.4595
... ...
19373 -0.3984
19374 -0.3799
19375 -0.3628
19376 -0.3506
19377 -0.3347
[9135 rows x 1 columns],
PORT4_CHN1
14230 NaN
14231 -0.0547
14232 -0.0942
14233 NaN
14234 -0.1716
... ...
23360 -2.2869
23361 -2.3169
23362 NaN
23363 NaN
23364 -2.3784
[9135 rows x 1 columns],
PORT4_CHN1
14611 -0.0034
14612 NaN
14613 -0.0029
14614 -0.0046
14615 NaN
... ...
23741 0.5059
23742 0.4607
23743 NaN
23744 NaN
23745 0.3228
[9135 rows x 1 columns],
PORT4_CHN1
15149 0.5410
15150 0.5278
15151 NaN
15152 0.4967
15153 NaN
... ...
24279 -0.6494
24280 -0.6082
24281 NaN
24282 -0.5249
24283 NaN
[9135 rows x 1 columns],
PORT6_CHN1
0 -0.4806
1 -0.4819
2 -0.4822
3 -0.4819
4 -0.4812
... ...
7075 0.7083
7076 0.7065
7077 0.7017
7078 0.6946
7079 0.6829
[7080 rows x 1 columns],
PORT6_CHN1
2555 -0.0112
2556 NaN
2557 0.0020
2558 0.0059
2559 0.0088
... ...
11685 -0.3695
11686 -0.3698
11687 -0.3691
11688 -0.3706
11689 NaN
[9135 rows x 1 columns],
PORT6_CHN1
23490 -0.3710
23491 -0.3452
23492 -0.3196
23493 -0.2863
23494 -0.2471
... ...
30445 -0.6162
30446 -0.6157
30447 -0.6145
30448 -0.6157
30449 -0.6144
[6960 rows x 1 columns],
PORT7_CHN1
17192 -0.6129
17193 -0.6315
17194 -0.6489
17195 -0.6661
17196 -0.6812
... ...
26322 -0.3164
26323 -0.3174
26324 -0.3167
26325 -0.3193
26326 -0.3223
[9135 rows x 1 columns],
PORT7_CHN1
23083 0.2059
23084 NaN
23085 0.1067
23086 0.0597
23087 0.0089
... ...
30445 0.1777
30446 0.1780
30447 0.1772
30448 0.1820
30449 0.1855
[7367 rows x 1 columns]]
part2-1 B 과정과 동일
C) Organize the result data to data type “dict” with Key and Value
parse_to_dict2 = [] #딕셔너리화할 파싱 데이터를 저장할 리스트
for index in range(len(parsed_data2)): #column을 돌며
parse_to_dict2.append(parsed_data2[index].to_dict()) #딕셔너리화
parse_to_dict2 #딕셔너리화한 파싱 데이터 확인
[{' PORT4_CHN1': {0: nan,
1: -0.2104,
2: -0.2103,
3: -0.2119,
4: -0.2148,
5: -0.2156,
6: nan,
7: nan,
8: -0.2209,
9: -0.2227,
10: -0.2253,
11: nan,
12: -0.2278,
13: nan,
14: nan,
15: -0.2322,
16: nan,
17: -0.2349,
18: nan,
19: nan,
20: nan,
21: nan,
22: -0.2374,
23: -0.2383,
24: -0.24,
25: nan,
26: nan,
27: -0.2393,
28: -0.2397,
29: nan,
30: -0.2378,
31: -0.2373,
32: nan,
33: nan,
34: -0.2358,
35: nan,
36: -0.2373,
37: nan,
38: -0.2339,
39: nan,
40: -0.2311,
41: -0.23,
42: -0.23,
43: -0.2285,
44: -0.2261,
45: nan,
46: -0.2231,
47: -0.2227,
48: nan,
49: -0.2202,
50: -0.2183,
51: nan,
52: -0.2158,
53: -0.2144,
54: -0.2119,
55: nan,
56: nan,
57: nan,
58: -0.2056,
59: -0.2056,
60: -0.2021,
61: -0.2021,
62: -0.2,
63: -0.1989999999999999,
64: -0.1973,
65: nan,
66: -0.1947,
67: nan,
68: nan,
69: -0.1875,
70: nan,
71: nan,
72: -0.186,
73: nan,
74: -0.1843,
75: -0.1831,
76: nan,
77: -0.1821,
78: -0.1814,
79: -0.1786,
80: -0.1787,
81: -0.1763,
82: nan,
83: nan,
84: nan,
85: -0.1729,
86: nan,
87: -0.1733,
88: -0.1738,
89: -0.1748,
90: -0.1743,
91: nan,
92: -0.1747,
93: nan,
94: nan,
95: nan,
96: -0.1755,
97: nan,
98: nan,
99: -0.1758,
100: -0.1763,
101: nan,
102: nan,
103: -0.1787,
104: -0.1787,
105: -0.1777,
106: nan,
107: -0.1807,
108: -0.1807,
109: nan,
110: -0.1826,
111: -0.1841,
112: -0.1843,
113: nan,
114: -0.1879,
115: -0.1899,
116: -0.1889999999999999,
117: nan,
118: -0.1914,
119: -0.1924,
120: -0.1924,
121: -0.1952,
122: -0.1962,
123: nan,
124: nan,
125: -0.2002,
126: -0.2017,
127: nan,
128: nan,
129: nan,
130: -0.2083,
131: -0.21,
132: nan,
133: nan,
134: nan,
135: nan,
136: -0.2156,
137: -0.2173,
138: -0.2188,
139: nan,
140: nan,
141: nan,
142: -0.2273,
143: nan,
144: -0.2283,
145: -0.2302,
146: nan,
147: -0.2314,
148: -0.2344,
149: -0.2358,
150: nan,
151: -0.2393,
152: -0.2432,
153: nan,
154: -0.2416,
155: -0.2441,
156: nan,
157: nan,
158: -0.2451,
159: -0.2466,
160: -0.2473,
161: -0.2478,
162: -0.2505,
163: -0.2485,
164: nan,
165: nan,
166: -0.2493,
167: nan,
168: nan,
169: -0.251,
170: -0.2505,
171: -0.252,
172: -0.252,
173: nan,
174: -0.252,
175: -0.25,
176: -0.2493,
177: nan,
178: -0.248,
179: -0.2485,
180: nan,
181: nan,
182: -0.2448,
183: -0.2437,
184: -0.2417,
185: -0.2405,
186: -0.2393,
187: -0.2383,
188: -0.2373,
189: nan,
190: -0.2344,
191: nan,
192: -0.2324,
193: nan,
194: -0.2289999999999999,
195: -0.2251,
196: -0.2253,
197: nan,
198: -0.2202,
199: -0.2183,
200: nan,
201: -0.2131,
202: -0.2122,
203: -0.2087,
204: -0.2068,
205: -0.2036,
206: -0.2023,
207: -0.2019,
208: -0.1992,
209: -0.1963,
210: nan,
211: -0.1931,
212: nan,
213: nan,
214: nan,
215: -0.1846,
216: -0.1836,
217: nan,
218: -0.1807,
219: nan,
220: -0.1775,
221: -0.1772,
222: -0.1763,
223: nan,
224: nan,
225: -0.1731,
226: nan,
227: nan,
228: -0.1704,
229: nan,
230: -0.1697,
231: -0.168,
232: nan,
233: -0.168,
234: -0.1685,
235: -0.1678,
236: -0.1678,
237: -0.1689,
238: nan,
239: nan,
240: nan,
241: nan,
242: -0.1709,
243: -0.1719,
244: -0.1699,
245: -0.1724,
246: -0.1736,
247: nan,
248: -0.1716,
249: -0.1727,
250: -0.1733,
251: -0.1748,
252: nan,
253: -0.1763,
254: -0.1782,
255: nan,
256: -0.1792,
257: -0.1794,
258: -0.1797,
259: nan,
260: -0.1801,
261: -0.1826,
262: -0.1836,
263: -0.1846,
264: -0.1855,
265: -0.1863,
266: nan,
267: -0.1885,
268: nan,
269: -0.1929,
270: -0.1934,
271: -0.1948,
272: -0.1958,
273: -0.1978,
274: nan,
275: nan,
276: -0.1992,
277: -0.2002,
278: -0.2002,
279: -0.2007,
280: -0.2021,
281: -0.2021,
282: -0.2031,
283: -0.2041,
284: -0.2031,
285: -0.2024,
286: -0.2039,
287: nan,
288: -0.207,
289: -0.207,
290: nan,
291: -0.2068,
292: nan,
293: -0.209,
294: nan,
295: -0.207,
296: -0.2068,
297: nan,
298: -0.208,
299: -0.209,
300: nan,
301: -0.2075,
302: -0.2083,
303: -0.2079,
304: nan,
305: -0.2075,
306: nan,
307: -0.2095,
308: -0.2109,
309: -0.2104,
310: -0.2104,
311: nan,
312: -0.2124,
313: -0.2119,
314: -0.2107,
315: -0.2104,
316: -0.2103,
317: nan,
318: -0.209,
319: -0.2075,
320: nan,
321: nan,
322: -0.2083,
323: -0.2085,
324: nan,
325: -0.2065,
326: -0.207,
327: nan,
328: -0.2061,
329: nan,
330: nan,
331: -0.2048,
332: -0.207,
333: -0.2039,
334: -0.2041,
335: nan,
336: -0.2034,
337: -0.2041,
338: nan,
339: -0.2039,
340: -0.2039,
341: -0.2021,
342: nan,
343: nan,
344: -0.2014,
345: nan,
346: nan,
347: -0.1987,
348: -0.1973,
349: -0.1963,
350: nan,
351: -0.1958,
352: nan,
353: -0.1963,
354: nan,
355: -0.1958,
356: -0.1943,
357: nan,
358: -0.1936,
359: -0.1943,
360: nan,
361: nan,
362: -0.1929,
363: nan,
364: -0.1909,
365: nan,
366: -0.1919,
367: nan,
368: nan,
369: -0.1904,
370: -0.1919,
371: nan,
372: -0.1892,
373: nan,
374: -0.1892,
375: -0.1897,
376: -0.1887,
377: -0.1892,
378: nan,
379: -0.1895,
380: nan,
381: -0.1892,
382: -0.1887,
383: -0.1889999999999999,
384: -0.1875,
385: -0.1866,
386: -0.1865,
387: -0.1865,
388: -0.1846,
389: -0.1836,
390: nan,
391: -0.1812,
392: nan,
393: nan,
394: nan,
395: -0.1777,
396: -0.1775,
397: nan,
398: -0.1753,
399: nan,
400: nan,
401: -0.1746,
402: -0.1753,
403: -0.1729,
404: -0.1748,
405: -0.1748,
406: -0.1725,
407: -0.1719,
408: nan,
409: -0.1704,
410: -0.1699,
411: -0.1704,
412: -0.1699,
413: nan,
414: -0.1714,
415: -0.1704,
416: nan,
417: nan,
418: nan,
419: -0.1689,
420: -0.1689,
421: nan,
422: nan,
423: -0.1658,
424: -0.1665,
425: nan,
426: nan,
427: -0.1682,
428: -0.1685,
429: nan,
430: -0.168,
431: nan,
432: -0.1692,
433: -0.1697,
434: -0.1707,
435: nan,
436: nan,
437: -0.1733,
438: -0.1743,
439: -0.1746,
440: -0.1753,
441: -0.1785,
442: -0.1792,
443: -0.1804,
444: -0.1816,
445: -0.1816,
446: -0.1831,
447: nan,
448: nan,
449: nan,
450: -0.1875,
451: -0.1904,
452: nan,
453: -0.1934,
454: nan,
455: -0.1951,
456: -0.1968,
457: nan,
458: nan,
459: -0.1992,
460: -0.2009,
461: nan,
462: -0.2026,
463: -0.2026,
464: -0.2021,
465: -0.2051,
466: -0.2041,
467: -0.2048,
468: -0.2073,
469: nan,
470: nan,
471: -0.2109,
472: nan,
473: nan,
474: nan,
475: nan,
476: nan,
477: -0.2147,
478: -0.2153,
479: -0.2144,
480: nan,
481: -0.2148,
482: -0.2153,
483: nan,
484: -0.2144,
485: -0.2144,
486: -0.2139,
487: -0.2129,
488: nan,
489: -0.2144,
490: -0.2137,
491: -0.2126,
492: -0.2119,
493: nan,
494: -0.2124,
495: nan,
496: -0.2109,
497: -0.21,
498: -0.208,
499: -0.2061,
500: -0.2065,
501: -0.2056,
502: -0.2039,
503: -0.2031,
504: -0.2024,
505: -0.2015,
506: -0.1997,
507: -0.2017,
508: -0.2012,
509: -0.2017,
510: -0.1989999999999999,
511: -0.1981,
512: -0.1982,
513: nan,
514: -0.1973,
515: -0.1947,
516: -0.1941,
517: -0.1924,
518: nan,
519: -0.1889999999999999,
520: nan,
521: -0.186,
522: -0.1895,
523: nan,
524: -0.1987,
525: -0.1982,
526: -0.2001,
527: -0.2004,
528: -0.2012,
529: nan,
530: -0.1963,
531: -0.1943,
532: -0.1924,
533: -0.1885,
534: -0.1868,
535: nan,
536: -0.187,
537: -0.1868,
538: -0.1846,
539: -0.1826,
540: nan,
541: -0.1807,
542: nan,
543: nan,
544: -0.1825,
545: nan,
546: -0.1826,
547: nan,
548: -0.1824,
549: nan,
550: -0.1826,
551: -0.1831,
552: -0.183,
553: -0.1831,
554: -0.1816,
555: -0.183,
556: nan,
557: -0.1831,
558: -0.1831,
559: -0.1836,
560: -0.1821,
561: nan,
562: nan,
563: -0.1807,
564: -0.1807,
565: -0.1807,
566: -0.1804,
567: nan,
568: -0.179,
569: -0.1804,
570: -0.1804,
571: -0.1797,
572: nan,
573: nan,
574: nan,
575: nan,
576: -0.1831,
577: nan,
578: nan,
579: -0.1844,
580: -0.1853,
581: nan,
582: nan,
583: -0.1885,
584: -0.1899,
585: -0.1908,
586: nan,
587: nan,
588: nan,
589: -0.1951,
590: nan,
591: -0.1973,
592: -0.1992,
593: -0.1997,
594: nan,
595: -0.2009,
596: -0.2029,
597: -0.2021,
598: -0.2012,
599: -0.2007,
600: -0.2017,
601: -0.2019,
602: nan,
603: -0.2007,
604: -0.2007,
605: -0.2002,
606: -0.2012,
607: -0.2007,
608: nan,
609: -0.1997,
610: -0.1982,
611: nan,
612: -0.1973,
613: nan,
614: -0.1982,
615: nan,
616: -0.1973,
617: -0.1963,
618: -0.1948,
619: -0.1936,
620: -0.1936,
621: -0.1941,
622: -0.1943,
623: -0.1914,
624: nan,
625: nan,
626: -0.1885,
627: -0.1863,
628: nan,
629: -0.1846,
630: -0.1831,
631: nan,
632: -0.1807,
633: nan,
634: -0.1777,
635: nan,
636: nan,
637: nan,
638: nan,
639: -0.1714,
640: -0.1699,
641: -0.1675,
642: -0.1669999999999999,
643: -0.1646,
644: -0.1648,
645: -0.1643,
646: nan,
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648: nan,
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650: nan,
651: nan,
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653: nan,
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657: -0.166,
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661: -0.1685,
662: -0.168,
663: -0.1687,
664: -0.1707,
665: nan,
666: nan,
667: -0.1755,
668: -0.176,
669: -0.1797,
670: -0.1821,
671: -0.1855,
672: nan,
673: nan,
674: nan,
675: nan,
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680: nan,
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684: nan,
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715: nan,
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724: nan,
725: -0.1636,
726: nan,
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728: nan,
729: -0.1489,
730: -0.1433,
731: nan,
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733: -0.1338,
734: -0.1309,
735: -0.1282,
736: -0.1245,
737: -0.1219,
738: -0.1187,
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740: -0.1118,
741: -0.1089,
742: nan,
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744: nan,
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754: -0.1006,
755: -0.1,
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757: -0.1021,
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760: -0.104,
761: -0.1064,
762: -0.1069,
763: nan,
764: -0.1118,
765: -0.113,
766: -0.1162,
767: nan,
768: nan,
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770: -0.1255,
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783: nan,
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785: nan,
786: nan,
787: -0.1831,
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24029: -2.3542,
24030: -2.3071,
24031: -2.2574,
24032: -2.2023,
24033: -2.147,
24034: -2.0874,
24035: -2.0247,
24036: -1.9617,
24037: -1.8938,
24038: nan,
24039: -1.7532,
24040: -1.6818,
24041: -1.6025,
24042: nan,
24043: nan,
24044: -1.3809,
24045: -1.3108,
24046: nan,
24047: -1.1719,
24048: -1.1033,
24049: -1.0345,
24050: -0.9675,
24051: -0.9006,
24052: -0.8340000000000001,
24053: -0.7695,
24054: -0.7078,
24055: -0.6475,
24056: -0.5884,
24057: -0.5332,
24058: -0.4775,
24059: nan,
24060: -0.3661,
24061: -0.3132,
24062: -0.2649,
24063: nan,
24064: nan,
24065: -0.1255,
24066: nan,
24067: -0.0339,
24068: nan,
24069: 0.0483,
24070: 0.0876,
24071: nan,
24072: 0.1641,
24073: 0.2019999999999999,
24074: 0.2366,
24075: 0.2703,
24076: 0.3038,
24077: 0.3396,
24078: 0.37,
24079: 0.3987,
24080: nan,
24081: 0.457,
24082: nan,
...}}]
part2-1 C과정과 동일
D) Save the result as a json file.
#json.dump
#json.dump
import json
dict =(parse_to_dict2)
with open('./data2.json','w')as f:
json.dump(dict,f,ensure_ascii=False,indent=4)
part2-1 D 과정과 동일
part2-1 port6, part2-2 모든 포트에서 처럼, 데이터의 평균 혹은 중간 값과 같은 값을 가진 인덱스가 나오지 않을 경우, 합리적인 근거를 들어 모델을 구현해야되는데, 나는 이 과정에서 평균 혹은 중간 값과 가장 차이가 적은 값을 가진 인덱스를 뽑아내는 것이 이후에 데이터를 사용할때도 가장 오차율이 적게 활용될 수 있을 것이라 생각하여 이와 같은 기준을 잡고 구현하였다. json파일을 확인해 보았을때 C에서 처리한 딕셔너리화된 데이터가 그대로 저장되어 나오는 것을 확인할 수 있었고, 또 다른 요구 조건인 전 후 15%를 추출하는 것 또한 csv파일을 통해 비교해 보았을때 잘 그룹화 되어 추출되는 것을 확인할 수 있었다.